Overview

Dataset statistics

Number of variables39
Number of observations491
Missing cells2092
Missing cells (%)10.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory143.0 KiB
Average record size in memory298.3 B

Variable types

CAT24
NUM11
DATE2
BOOL2

Warnings

avobstime has a high cardinality: 183 distinct values High cardinality
avcomment has a high cardinality: 342 distinct values High cardinality
eded_time has a high cardinality: 189 distinct values High cardinality
ch_diff_cm is highly correlated with ch_max_cm and 1 other fieldsHigh correlation
ch_max_cm is highly correlated with ch_diff_cmHigh correlation
ch_min_cm is highly correlated with ch_diff_cmHigh correlation
deb_hwy_w is highly correlated with deb_hwyHigh correlation
deb_hwy is highly correlated with deb_hwy_wHigh correlation
avtrigger is highly correlated with avtriggertypHigh correlation
avtriggertyp is highly correlated with avtriggerHigh correlation
edcr_ip is highly correlated with edcr_userHigh correlation
edcr_user is highly correlated with edcr_ipHigh correlation
eded_ip is highly correlated with eded_userHigh correlation
eded_user is highly correlated with eded_ipHigh correlation
avtype has 6 (1.2%) missing values Missing
avtriggertyp has 36 (7.3%) missing values Missing
avtrigger has 45 (9.2%) missing values Missing
avrelativesize has 17 (3.5%) missing values Missing
avdestructivesize has 50 (10.2%) missing values Missing
avinterface has 60 (12.2%) missing values Missing
sz_vert has 21 (4.3%) missing values Missing
sz_horiz has 38 (7.7%) missing values Missing
sz_aspect has 386 (78.6%) missing values Missing
term_longpath has 30 (6.1%) missing values Missing
term_shortPath has 165 (33.6%) missing values Missing
term_detail has 184 (37.5%) missing values Missing
term_moist has 113 (23.0%) missing values Missing
avcomment has 129 (26.3%) missing values Missing
edcr_user has 127 (25.9%) missing values Missing
edcr_ip has 98 (20.0%) missing values Missing
eded_user has 294 (59.9%) missing values Missing
eded_ip has 293 (59.7%) missing values Missing
avcomment is uniformly distributed Uniform
avrid has unique values Unique
sz_avgslope has 403 (82.1%) zeros Zeros
sz_elev_m has 242 (49.3%) zeros Zeros
term_elev_m has 208 (42.4%) zeros Zeros
av_vert_m has 248 (50.5%) zeros Zeros
crown_width_m has 342 (69.7%) zeros Zeros
ch_max_cm has 341 (69.5%) zeros Zeros
ch_min_cm has 341 (69.5%) zeros Zeros
ch_diff_cm has 342 (69.7%) zeros Zeros
deb_rail_w has 465 (94.7%) zeros Zeros
deb_rail_d has 465 (94.7%) zeros Zeros

Reproduction

Analysis started2023-01-04 13:52:11.147170
Analysis finished2023-01-04 13:52:42.686301
Duration31.54 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

avrid
Real number (ℝ≥0)

UNIQUE

Distinct491
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean253.4032587
Minimum1
Maximum508
Zeros0
Zeros (%)0.0%
Memory size3.8 KiB
2023-01-04T08:52:42.837266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile26.5
Q1127.5
median250
Q3380.5
95-th percentile483.5
Maximum508
Range507
Interquartile range (IQR)253

Descriptive statistics

Standard deviation147.180551
Coefficient of variation (CV)0.580815542
Kurtosis-1.203903522
Mean253.4032587
Median Absolute Deviation (MAD)127
Skewness0.01891273965
Sum124421
Variance21662.1146
MonotocityNot monotonic
2023-01-04T08:52:43.010444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
110.2%
 
34810.2%
 
34610.2%
 
34510.2%
 
34410.2%
 
34310.2%
 
34210.2%
 
34110.2%
 
33910.2%
 
33810.2%
 
Other values (481)48198.0%
 
ValueCountFrequency (%) 
110.2%
 
210.2%
 
310.2%
 
410.2%
 
510.2%
 
ValueCountFrequency (%) 
50810.2%
 
50710.2%
 
50610.2%
 
50510.2%
 
50410.2%
 

avpathid
Categorical

Distinct48
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
PATH1163
105 
INFINITY
58 
SHED10
35 
SHED07W
28 
SHED11
 
23
Other values (43)
242 
ValueCountFrequency (%) 
PATH116310521.4%
 
INFINITY5811.8%
 
SHED10357.1%
 
SHED07W285.7%
 
SHED11234.7%
 
JAKES204.1%
 
SHED07E173.5%
 
SHED08163.3%
 
JAVA153.1%
 
AP1163H142.9%
 
Other values (38)16032.6%
 
2023-01-04T08:52:43.200110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique9 ?
Unique (%)1.8%
2023-01-04T08:52:43.381931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length7
Mean length6.910386965
Min length2
Distinct209
Distinct (%)42.6%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
Minimum2005-01-17 00:00:00
Maximum2022-03-27 00:00:00
2023-01-04T08:52:43.551815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:43.877098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

avobstime
Categorical

HIGH CARDINALITY

Distinct183
Distinct (%)37.3%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
12:00:00
 
22
16:00:00
 
21
13:00:00
 
19
10:00:00
 
19
11:00:00
 
15
Other values (178)
395 
ValueCountFrequency (%) 
12:00:00224.5%
 
16:00:00214.3%
 
13:00:00193.9%
 
10:00:00193.9%
 
11:00:00153.1%
 
15:30:00122.4%
 
14:30:00122.4%
 
10:30:00122.4%
 
11:30:00122.4%
 
09:00:00112.2%
 
Other values (173)33668.4%
 
2023-01-04T08:52:44.114272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique111 ?
Unique (%)22.6%
2023-01-04T08:52:44.256618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length8
Min length8

avobserverid
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
stein_01
307 
clark_01
156 
dunda_01
 
28
ValueCountFrequency (%) 
stein_0130762.5%
 
clark_0115631.8%
 
dunda_01285.7%
 
2023-01-04T08:52:44.391923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2023-01-04T08:52:44.505900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:44.600511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length8
Min length8

avtype
Categorical

MISSING

Distinct9
Distinct (%)1.9%
Missing6
Missing (%)1.2%
Memory size3.8 KiB
SS
234 
WL
78 
U
73 
L
44 
HS
29 
Other values (4)
27 
ValueCountFrequency (%) 
SS23447.7%
 
WL7815.9%
 
U7314.9%
 
L449.0%
 
HS295.9%
 
WS204.1%
 
C40.8%
 
SU20.4%
 
GS10.2%
 
(Missing)61.2%
 
2023-01-04T08:52:44.735587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.2%
2023-01-04T08:52:44.841869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:45.027508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length1.765784114
Min length1

avtriggertyp
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.7%
Missing36
Missing (%)7.3%
Memory size3.8 KiB
5
421 
1
 
23
2
 
11
ValueCountFrequency (%) 
542185.7%
 
1234.7%
 
2112.2%
 
(Missing)367.3%
 
2023-01-04T08:52:45.228161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2023-01-04T08:52:45.343236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:45.443897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

avtrigger
Categorical

HIGH CORRELATION
MISSING

Distinct11
Distinct (%)2.5%
Missing45
Missing (%)9.2%
Memory size3.8 KiB
NU
202 
N
176 
NC
25 
AX
 
18
NL
 
9
Other values (6)
 
16
ValueCountFrequency (%) 
NU20241.1%
 
N17635.8%
 
NC255.1%
 
AX183.7%
 
NL91.8%
 
AS91.8%
 
AH30.6%
 
NS10.2%
 
Asr10.2%
 
AE10.2%
 
(Missing)459.2%
 
2023-01-04T08:52:45.647322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique4 ?
Unique (%)0.9%
2023-01-04T08:52:45.808469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length1.735234216
Min length1

avrelativesize
Categorical

MISSING

Distinct6
Distinct (%)1.3%
Missing17
Missing (%)3.5%
Memory size3.8 KiB
R2
193 
R1
106 
U
89 
R3
69 
R4
 
16
ValueCountFrequency (%) 
R219339.3%
 
R110621.6%
 
U8918.1%
 
R36914.1%
 
R4163.3%
 
R510.2%
 
(Missing)173.5%
 
2023-01-04T08:52:46.033920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.2%
2023-01-04T08:52:46.163831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:46.299331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length1.853360489
Min length1

avdestructivesize
Categorical

MISSING

Distinct8
Distinct (%)1.8%
Missing50
Missing (%)10.2%
Memory size3.8 KiB
D2
183 
D1.5
98 
U
50 
D1
43 
D2.5
34 
Other values (3)
33 
ValueCountFrequency (%) 
D218337.3%
 
D1.59820.0%
 
U5010.2%
 
D1438.8%
 
D2.5346.9%
 
D3295.9%
 
D420.4%
 
D3.520.4%
 
(Missing)5010.2%
 
2023-01-04T08:52:46.495650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2023-01-04T08:52:46.604158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:46.751014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length2
Mean length2.545824847
Min length1

avinterface
Categorical

MISSING

Distinct5
Distinct (%)1.2%
Missing60
Missing (%)12.2%
Memory size3.8 KiB
U
174 
I
108 
O
98 
S
40 
G
 
11
ValueCountFrequency (%) 
U17435.4%
 
I10822.0%
 
O9820.0%
 
S408.1%
 
G112.2%
 
(Missing)6012.2%
 
2023-01-04T08:52:46.921894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2023-01-04T08:52:47.043458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:47.179258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.244399185
Min length1

sz_vert
Categorical

MISSING

Distinct4
Distinct (%)0.9%
Missing21
Missing (%)4.3%
Memory size3.8 KiB
T
252 
U
121 
M
84 
B
 
13
ValueCountFrequency (%) 
T25251.3%
 
U12124.6%
 
M8417.1%
 
B132.6%
 
(Missing)214.3%
 
2023-01-04T08:52:47.336225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2023-01-04T08:52:47.592848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:47.696767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.085539715
Min length1

sz_horiz
Categorical

MISSING

Distinct4
Distinct (%)0.9%
Missing38
Missing (%)7.7%
Memory size3.8 KiB
U
155 
C
144 
L
81 
R
73 
ValueCountFrequency (%) 
U15531.6%
 
C14429.3%
 
L8116.5%
 
R7314.9%
 
(Missing)387.7%
 
2023-01-04T08:52:47.838248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2023-01-04T08:52:47.930613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:48.031253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.154786151
Min length1

sz_avgslope
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.99185336
Minimum0
Maximum45
Zeros403
Zeros (%)82.1%
Memory size3.8 KiB
2023-01-04T08:52:48.156230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile40
Maximum45
Range45
Interquartile range (IQR)0

Descriptive statistics

Standard deviation15.02242999
Coefficient of variation (CV)2.148561936
Kurtosis0.9394864018
Mean6.99185336
Median Absolute Deviation (MAD)0
Skewness1.701270512
Sum3433
Variance225.6734029
MonotocityNot monotonic
2023-01-04T08:52:48.266110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
040382.1%
 
40387.7%
 
38142.9%
 
35112.2%
 
3781.6%
 
4571.4%
 
4251.0%
 
3440.8%
 
3910.2%
 
ValueCountFrequency (%) 
040382.1%
 
3440.8%
 
35112.2%
 
3781.6%
 
38142.9%
 
ValueCountFrequency (%) 
4571.4%
 
4251.0%
 
40387.7%
 
3910.2%
 
38142.9%
 

sz_elev_m
Real number (ℝ≥0)

ZEROS

Distinct116
Distinct (%)23.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean947.7169043
Minimum0
Maximum5000
Zeros242
Zeros (%)49.3%
Memory size3.8 KiB
2023-01-04T08:52:48.410604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1280
Q31866.5
95-th percentile2165
Maximum5000
Range5000
Interquartile range (IQR)1866.5

Descriptive statistics

Standard deviation960.7033772
Coefficient of variation (CV)1.013702903
Kurtosis-1.321936134
Mean947.7169043
Median Absolute Deviation (MAD)1038
Skewness0.1912887326
Sum465329
Variance922950.9789
MonotocityNot monotonic
2023-01-04T08:52:48.585366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
024249.3%
 
1830122.4%
 
1818122.4%
 
193981.6%
 
189071.4%
 
170071.4%
 
180071.4%
 
169771.4%
 
200051.0%
 
195051.0%
 
Other values (106)17936.5%
 
ValueCountFrequency (%) 
024249.3%
 
125010.2%
 
127310.2%
 
128030.6%
 
130610.2%
 
ValueCountFrequency (%) 
500010.2%
 
234810.2%
 
234510.2%
 
231810.2%
 
228510.2%
 

sz_aspect
Categorical

MISSING

Distinct7
Distinct (%)6.7%
Missing386
Missing (%)78.6%
Memory size3.8 KiB
N
45 
SE
18 
S
17 
SW
11 
E
Other values (2)
ValueCountFrequency (%) 
N459.2%
 
SE183.7%
 
S173.5%
 
SW112.2%
 
E91.8%
 
NE40.8%
 
W10.2%
 
(Missing)38678.6%
 
2023-01-04T08:52:48.740390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)1.0%
2023-01-04T08:52:48.840502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:48.966505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length2.639511202
Min length1

term_elev_m
Real number (ℝ≥0)

ZEROS

Distinct112
Distinct (%)22.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean878.1425662
Minimum0
Maximum4550
Zeros208
Zeros (%)42.4%
Memory size3.8 KiB
2023-01-04T08:52:49.124546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1310
Q31460
95-th percentile1787.5
Maximum4550
Range4550
Interquartile range (IQR)1460

Descriptive statistics

Standard deviation811.5932605
Coefficient of variation (CV)0.9242158298
Kurtosis0.9436597264
Mean878.1425662
Median Absolute Deviation (MAD)390
Skewness0.464230436
Sum431168
Variance658683.6204
MonotocityNot monotonic
2023-01-04T08:52:49.293663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
020842.4%
 
1400316.3%
 
1515112.2%
 
1460102.0%
 
1370102.0%
 
145591.8%
 
139481.6%
 
133371.4%
 
169771.4%
 
135371.4%
 
Other values (102)18337.3%
 
ValueCountFrequency (%) 
020842.4%
 
103010.2%
 
106710.2%
 
115210.2%
 
119010.2%
 
ValueCountFrequency (%) 
455010.2%
 
440030.6%
 
215010.2%
 
213410.2%
 
210010.2%
 

av_vert_m
Real number (ℝ≥0)

ZEROS

Distinct151
Distinct (%)30.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.9450102
Minimum0
Maximum2285
Zeros248
Zeros (%)50.5%
Memory size3.8 KiB
2023-01-04T08:52:49.461480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3344.5
95-th percentile726
Maximum2285
Range2285
Interquartile range (IQR)344.5

Descriptive statistics

Standard deviation320.9309021
Coefficient of variation (CV)1.597108093
Kurtosis12.6963222
Mean200.9450102
Median Absolute Deviation (MAD)0
Skewness2.92749673
Sum98664
Variance102996.6439
MonotocityNot monotonic
2023-01-04T08:52:49.627717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
024850.5%
 
6091.8%
 
30351.0%
 
43051.0%
 
3051.0%
 
37040.8%
 
49040.8%
 
30040.8%
 
18240.8%
 
76540.8%
 
Other values (141)19940.5%
 
ValueCountFrequency (%) 
024850.5%
 
810.2%
 
1510.2%
 
3051.0%
 
3110.2%
 
ValueCountFrequency (%) 
228510.2%
 
204210.2%
 
201510.2%
 
195110.2%
 
193910.2%
 

crown_width_m
Real number (ℝ≥0)

ZEROS

Distinct37
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.05906314
Minimum0
Maximum1972
Zeros342
Zeros (%)69.7%
Memory size3.8 KiB
2023-01-04T08:52:49.784634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q320
95-th percentile150
Maximum1972
Range1972
Interquartile range (IQR)20

Descriptive statistics

Standard deviation117.0268527
Coefficient of variation (CV)3.65035161
Kurtosis160.0694173
Mean32.05906314
Median Absolute Deviation (MAD)0
Skewness10.78193889
Sum15741
Variance13695.28426
MonotocityNot monotonic
2023-01-04T08:52:49.941572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%) 
034269.7%
 
30234.7%
 
20234.7%
 
60132.6%
 
40122.4%
 
100122.4%
 
50102.0%
 
20061.2%
 
7061.2%
 
15051.0%
 
Other values (27)397.9%
 
ValueCountFrequency (%) 
034269.7%
 
410.2%
 
510.2%
 
1010.2%
 
1520.4%
 
ValueCountFrequency (%) 
197210.2%
 
80010.2%
 
65010.2%
 
50010.2%
 
42410.2%
 

ch_max_cm
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct21
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.90835031
Minimum0
Maximum300
Zeros341
Zeros (%)69.5%
Memory size3.8 KiB
2023-01-04T08:52:50.093998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q330
95-th percentile120
Maximum300
Range300
Interquartile range (IQR)30

Descriptive statistics

Standard deviation44.06513907
Coefficient of variation (CV)2.011339898
Kurtosis8.9186803
Mean21.90835031
Median Absolute Deviation (MAD)0
Skewness2.751736118
Sum10757
Variance1941.736481
MonotocityNot monotonic
2023-01-04T08:52:50.221938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%) 
034169.5%
 
30316.3%
 
60244.9%
 
45132.6%
 
7591.8%
 
10091.8%
 
9091.8%
 
15081.6%
 
4081.6%
 
5071.4%
 
Other values (11)326.5%
 
ValueCountFrequency (%) 
034169.5%
 
1210.2%
 
1510.2%
 
2061.2%
 
2540.8%
 
ValueCountFrequency (%) 
30010.2%
 
25020.4%
 
20061.2%
 
18010.2%
 
15081.6%
 

ch_min_cm
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct22
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.6395112
Minimum0
Maximum300
Zeros341
Zeros (%)69.5%
Memory size3.8 KiB
2023-01-04T08:52:50.359172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q320
95-th percentile60
Maximum300
Range300
Interquartile range (IQR)20

Descriptive statistics

Standard deviation27.14921705
Coefficient of variation (CV)2.14796416
Kurtosis29.03558084
Mean12.6395112
Median Absolute Deviation (MAD)0
Skewness4.137396896
Sum6206
Variance737.0799867
MonotocityNot monotonic
2023-01-04T08:52:50.658719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%) 
034169.5%
 
30438.8%
 
20265.3%
 
60153.1%
 
25102.0%
 
50102.0%
 
1591.8%
 
1071.4%
 
10061.2%
 
7551.0%
 
Other values (12)193.9%
 
ValueCountFrequency (%) 
034169.5%
 
410.2%
 
510.2%
 
1071.4%
 
1210.2%
 
ValueCountFrequency (%) 
30010.2%
 
15010.2%
 
13010.2%
 
12030.6%
 
10061.2%
 

ch_diff_cm
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct40
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.17922607
Minimum0
Maximum210
Zeros342
Zeros (%)69.7%
Memory size3.8 KiB
2023-01-04T08:52:50.821000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q325
95-th percentile84
Maximum210
Range210
Interquartile range (IQR)25

Descriptive statistics

Standard deviation33.94804638
Coefficient of variation (CV)1.976110347
Kurtosis8.203302986
Mean17.17922607
Median Absolute Deviation (MAD)0
Skewness2.652361859
Sum8435
Variance1152.469853
MonotocityNot monotonic
2023-01-04T08:52:50.971644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%) 
034269.7%
 
30204.1%
 
45193.9%
 
75142.9%
 
25112.2%
 
38102.0%
 
6061.2%
 
2351.0%
 
6551.0%
 
2051.0%
 
Other values (30)5411.0%
 
ValueCountFrequency (%) 
034269.7%
 
710.2%
 
1310.2%
 
1520.4%
 
1820.4%
 
ValueCountFrequency (%) 
21010.2%
 
20020.4%
 
17510.2%
 
16510.2%
 
15010.2%
 

term_longpath
Categorical

MISSING

Distinct7
Distinct (%)1.5%
Missing30
Missing (%)6.1%
Memory size3.8 KiB
TK
163 
U
98 
BR
81 
TR
72 
MR
30 
Other values (2)
17 
ValueCountFrequency (%) 
TK16333.2%
 
U9820.0%
 
BR8116.5%
 
TR7214.7%
 
MR306.1%
 
SZ142.9%
 
-30.6%
 
(Missing)306.1%
 
2023-01-04T08:52:51.132574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2023-01-04T08:52:51.242609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:51.365716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length1.855397149
Min length1

term_shortPath
Categorical

MISSING

Distinct4
Distinct (%)1.2%
Missing165
Missing (%)33.6%
Memory size3.8 KiB
-
262 
BP
56 
MP
 
4
TP
 
4
ValueCountFrequency (%) 
-26253.4%
 
BP5611.4%
 
MP40.8%
 
TP40.8%
 
(Missing)16533.6%
 
2023-01-04T08:52:51.509380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2023-01-04T08:52:51.595540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:51.697098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.802443992
Min length1

term_detail
Categorical

MISSING

Distinct5
Distinct (%)1.6%
Missing184
Missing (%)37.5%
Memory size3.8 KiB
U
288 
1F
 
6
2F
 
5
-
 
4
3F
 
4
ValueCountFrequency (%) 
U28858.7%
 
1F61.2%
 
2F51.0%
 
-40.8%
 
3F40.8%
 
(Missing)18437.5%
 
2023-01-04T08:52:51.840299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2023-01-04T08:52:51.938481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:52.047547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.780040733
Min length1

term_moist
Categorical

MISSING

Distinct4
Distinct (%)1.1%
Missing113
Missing (%)23.0%
Memory size3.8 KiB
U
157 
W
97 
D
80 
M
44 
ValueCountFrequency (%) 
U15732.0%
 
W9719.8%
 
D8016.3%
 
M449.0%
 
(Missing)11323.0%
 
2023-01-04T08:52:52.191094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2023-01-04T08:52:52.285006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:52.391164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.460285132
Min length1

deb_rail
Boolean

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size491.0 B
False
453 
True
 
38
ValueCountFrequency (%) 
False45392.3%
 
True387.7%
 
2023-01-04T08:52:52.485855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

deb_rail_w
Real number (ℝ≥0)

ZEROS

Distinct14
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.018329939
Minimum0
Maximum130
Zeros465
Zeros (%)94.7%
Memory size3.8 KiB
2023-01-04T08:52:52.560006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.5
Maximum130
Range130
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.82481264
Coefficient of variation (CV)5.858711408
Kurtosis70.44125528
Mean2.018329939
Median Absolute Deviation (MAD)0
Skewness7.970272345
Sum991
Variance139.8261939
MonotocityNot monotonic
2023-01-04T08:52:52.690953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%) 
046594.7%
 
2071.4%
 
3061.2%
 
10020.4%
 
1020.4%
 
12010.2%
 
4010.2%
 
13010.2%
 
510.2%
 
410.2%
 
Other values (4)40.8%
 
ValueCountFrequency (%) 
046594.7%
 
410.2%
 
510.2%
 
710.2%
 
1020.4%
 
ValueCountFrequency (%) 
13010.2%
 
12010.2%
 
10020.4%
 
8010.2%
 
5010.2%
 

deb_rail_d
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1177189409
Minimum0
Maximum20
Zeros465
Zeros (%)94.7%
Memory size3.8 KiB
2023-01-04T08:52:52.814466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.2
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.025427582
Coefficient of variation (CV)8.710812154
Kurtosis296.880219
Mean0.1177189409
Median Absolute Deviation (MAD)0
Skewness16.10793276
Sum57.8
Variance1.051501725
MonotocityNot monotonic
2023-01-04T08:52:52.935667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
046594.7%
 
251.0%
 
0.240.8%
 
0.540.8%
 
140.8%
 
1.530.6%
 
810.2%
 
2010.2%
 
0.810.2%
 
0.710.2%
 
Other values (2)20.4%
 
ValueCountFrequency (%) 
046594.7%
 
0.240.8%
 
0.540.8%
 
0.710.2%
 
0.810.2%
 
ValueCountFrequency (%) 
2010.2%
 
810.2%
 
410.2%
 
310.2%
 
251.0%
 

deb_hwy
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size491.0 B
False
486 
True
 
5
ValueCountFrequency (%) 
False48699.0%
 
True51.0%
 
2023-01-04T08:52:53.039410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

deb_hwy_w
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
0
486 
20
 
2
30
 
2
10
 
1
ValueCountFrequency (%) 
048699.0%
 
2020.4%
 
3020.4%
 
1010.2%
 
2023-01-04T08:52:53.124587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.2%
2023-01-04T08:52:53.216164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:53.319045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length3
Mean length3.010183299
Min length3

deb_hwy_d
Categorical

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
0
486 
20
 
2
0.1
 
2
0.2
 
1
ValueCountFrequency (%) 
048699.0%
 
2020.4%
 
0.120.4%
 
0.210.2%
 
2023-01-04T08:52:53.442145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.2%
2023-01-04T08:52:53.685066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:53.775985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length3
Mean length3.00407332
Min length3

avcomment
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct342
Distinct (%)94.5%
Missing129
Missing (%)26.3%
Memory size3.8 KiB
Debris terminated above the rail.
 
5
Debris ran 2/3 path terminating above the grade.
 
5
Debris ran 1/2 path terminating above the rail.
 
3
Time is approximate. Debris fully across main 1 and partially across main 2.
 
3
Debris ran 1/2 path terminating above the grade.
 
2
Other values (337)
344 
ValueCountFrequency (%) 
Debris terminated above the rail.51.0%
 
Debris ran 2/3 path terminating above the grade.51.0%
 
Debris ran 1/2 path terminating above the rail.30.6%
 
Time is approximate. Debris fully across main 1 and partially across main 2.30.6%
 
Debris ran 1/2 path terminating above the grade.20.4%
 
Time is approximate. Debris across both mains.20.4%
 
Debris terminated on the grade covering Main 1.20.4%
 
Debris ran 2/3 path terminating above the rail.20.4%
 
Likely triggered by warm air temperatures and high elevation rain.20.4%
 
Debris terminated above the grade.20.4%
 
Other values (332)33468.0%
 
(Missing)12926.3%
 
2023-01-04T08:52:53.944877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique330 ?
Unique (%)91.2%
2023-01-04T08:52:54.142126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length456
Median length47
Mean length63.28309572
Min length3

edcr_user
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)1.1%
Missing127
Missing (%)25.9%
Memory size3.8 KiB
Ted
163 
Adam
160 
ed
31 
Steiner
 
10
ValueCountFrequency (%) 
Ted16333.2%
 
Adam16032.6%
 
ed316.3%
 
Steiner102.0%
 
(Missing)12725.9%
 
2023-01-04T08:52:54.300026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2023-01-04T08:52:54.396939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:54.496573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length3
Mean length3.344195519
Min length2
Distinct244
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
Minimum2016-03-17 11:50:56
Maximum2022-03-28 16:37:03
2023-01-04T08:52:54.642596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:54.820242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

edcr_ip
Categorical

HIGH CORRELATION
MISSING

Distinct20
Distinct (%)5.1%
Missing98
Missing (%)20.0%
Memory size3.8 KiB
10.173.132.4
58 
139.51.15.17
55 
139.51.15.19
38 
139.51.15.22
35 
139.51.15.20
32 
Other values (15)
175 
ValueCountFrequency (%) 
10.173.132.45811.8%
 
139.51.15.175511.2%
 
139.51.15.19387.7%
 
139.51.15.22357.1%
 
139.51.15.20326.5%
 
10.30.72.4285.7%
 
ed265.3%
 
10.173.132.2265.3%
 
64.91.61.64214.3%
 
10.173.132.15193.9%
 
Other values (10)5511.2%
 
(Missing)9820.0%
 
2023-01-04T08:52:55.008794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique4 ?
Unique (%)1.0%
2023-01-04T08:52:55.151284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length12
Mean length9.437881874
Min length2

eded_user
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)1.5%
Missing294
Missing (%)59.9%
Memory size3.8 KiB
Adam
97 
Ted
89 
ed
11 
ValueCountFrequency (%) 
Adam9719.8%
 
Ted8918.1%
 
ed112.2%
 
(Missing)29459.9%
 
2023-01-04T08:52:55.281164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2023-01-04T08:52:55.367002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:55.467763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length3
Mean length3.175152749
Min length2

eded_time
Categorical

HIGH CARDINALITY

Distinct189
Distinct (%)38.5%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
0000-00-00 00:00:00
293 
2019-01-02 00:00:00
 
11
2020-01-17 17:51:14
 
1
2022-03-08 01:00:55
 
1
2019-03-22 15:26:42
 
1
Other values (184)
184 
ValueCountFrequency (%) 
0000-00-00 00:00:0029359.7%
 
2019-01-02 00:00:00112.2%
 
2020-01-17 17:51:1410.2%
 
2022-03-08 01:00:5510.2%
 
2019-03-22 15:26:4210.2%
 
2019-12-17 13:33:4710.2%
 
2019-12-20 16:46:4910.2%
 
2019-12-23 23:47:0010.2%
 
2020-01-01 20:22:1810.2%
 
2020-01-01 19:44:4310.2%
 
Other values (179)17936.5%
 
2023-01-04T08:52:55.644133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique187 ?
Unique (%)38.1%
2023-01-04T08:52:55.794628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length19
Mean length19
Min length19

eded_ip
Categorical

HIGH CORRELATION
MISSING

Distinct10
Distinct (%)5.1%
Missing293
Missing (%)59.7%
Memory size3.8 KiB
64.91.61.64
73 
139.51.15.17
49 
139.51.15.19
26 
184.166.43.50
14 
server
11 
Other values (5)
25 
ValueCountFrequency (%) 
64.91.61.647314.9%
 
139.51.15.174910.0%
 
139.51.15.19265.3%
 
184.166.43.50142.9%
 
server112.2%
 
139.51.15.2271.4%
 
139.51.15.2061.2%
 
10.30.72.461.2%
 
170.49.113.1930.6%
 
170.49.113.1730.6%
 
(Missing)29359.7%
 
2023-01-04T08:52:55.931220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2023-01-04T08:52:56.037769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:56.185449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length3
Mean length6.362525458
Min length3

Interactions

2023-01-04T08:52:23.579517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:23.772723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:23.894091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:24.149024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:24.267369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:24.401980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:24.553446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:24.683206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:24.808128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:24.965139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:25.117739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:25.276256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:25.422236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:25.549442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:25.676923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:25.813039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:25.951120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:26.089095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:26.212510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:26.336747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:26.468991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:26.602663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:26.744926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:26.875986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:27.019623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:27.157649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:27.277656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:27.396555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:27.530287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:27.656408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:27.783134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:27.913481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:28.206328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:28.335033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:28.452089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:28.566870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:28.692622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:28.806838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:28.920584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:29.060844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:29.180640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:29.302562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:29.424093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:29.545240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:29.679625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:29.796219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:29.913868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:30.031798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:30.144775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:30.259437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:30.384622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:30.506486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:30.625328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:30.738809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:30.857429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:30.982958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:31.119923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:31.257778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:31.391862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:31.523701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:31.654499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:31.950039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:32.092779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:32.228155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:32.359823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:32.494675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:32.638517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:32.769862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:32.893207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:33.039404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:33.160970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:33.286544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:33.420622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:33.546666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:33.677482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:33.800200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:33.929064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:34.063222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:34.189319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:34.310889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:34.436590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:34.564230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:34.685701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:34.820028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:34.948248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:35.080870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:35.203428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:35.329584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:35.614256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:35.731969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:35.843434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:35.959187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:36.077511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:36.192241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:36.317648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:36.438988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:36.560312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:36.677012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:36.797547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:36.922588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:37.051403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:37.176076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:37.302559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:37.423918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:37.547816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:37.686882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:37.817627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:37.964024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:38.086930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:38.218819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:38.351714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:38.488204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:38.623451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:38.758279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:38.893530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:39.027740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:39.334523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:39.470678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:39.606411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:39.740471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:39.878102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-01-04T08:52:56.320325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-01-04T08:52:56.585942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-01-04T08:52:56.996451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-01-04T08:52:57.286359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2023-01-04T08:52:57.656314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2023-01-04T08:52:40.229897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:41.400908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:41.913609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-04T08:52:42.411555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Sample

First rows

avridavpathidavobsdateavobstimeavobserveridavtypeavtriggertypavtriggeravrelativesizeavdestructivesizeavinterfacesz_vertsz_horizsz_avgslopesz_elev_msz_aspectterm_elev_mav_vert_mcrown_width_mch_max_cmch_min_cmch_diff_cmterm_longpathterm_shortPathterm_detailterm_moistdeb_raildeb_rail_wdeb_rail_ddeb_hwydeb_hwy_wdeb_hwy_davcommentedcr_useredcr_timeedcr_ipeded_usereded_timeeded_ip
01SHED102012-03-2612:45:00stein_01WS5.0NLR1D1.5OBR00NaN000000TKNaNNaNNaNFalse0.00.0False0.00.0Wet slab released on SE aspect @ 5400 feet (1636 m) elevation. Debris ran 2/3rds path to at least 4,600 feet (1394 m) elevation.ed2016-03-17 11:50:5610.173.132.2NaN0000-00-00 00:00:00NaN
12PATH11632012-03-1608:30:00stein_01U5.0NUUD2UUU00NaN000000TKNaNNaNNaNFalse0.00.0False0.00.0Wet debris ran 2/3 path in main path to 4700 feet (1424 m) elevation. Upper SZ obscured.ed2016-03-17 11:50:5610.173.132.2NaN0000-00-00 00:00:00NaN
23CURLY2012-03-1608:34:00stein_01U5.0NUUD1UUU00NaN000000TRNaNNaNNaNFalse0.00.0False0.00.0Terminus at canyon floor elevation.\r\n\r\nDebris wet and relatively small amount.ed2016-03-17 11:50:5610.173.132.2NaN0000-00-00 00:00:00NaN
34SHED102012-03-1014:50:00stein_01WL5.0NaNR2D1IMR00NaN000000TKNaNNaNNaNFalse0.00.0False0.00.0D1.5ed2016-03-17 11:50:5610.173.132.2NaN0000-00-00 00:00:00NaN
45INFINITY2012-03-1014:54:00stein_01L5.0NR2D1ITC00NaN000000TKNaNNaNNaNFalse0.00.0False0.00.0D1.5- 1/2 Path.ed2016-03-17 11:50:5610.173.132.2NaN0000-00-00 00:00:00NaN
56SECOND2012-03-0511:45:00stein_01SS5.0NR2D1ITC00NaN000000NaNBPNaNNaNFalse0.00.0False0.00.0SS-NC-R2D 1.5-I on east aspect of Second Slide's looker's right ridge.ed2016-03-17 11:50:5610.173.132.2NaN0000-00-00 00:00:00NaN
67BURNOUT2012-03-0301:30:00stein_01SS5.0NR1D1IML00NaN000000NaNBPNaNNaNFalse0.00.0False0.00.0Debris did not make the rail grade.ed2016-03-17 11:50:5610.173.132.2NaN0000-00-00 00:00:00NaN
78SHED09L2012-02-2811:16:00stein_01SS5.0NSR2D2ITL02000NaN142457648453038MR-UMFalse0.00.0False0.00.0Likely triggered by avalanche in Shed 9 upper SZ.\nCrown width - 160 feet\nCrown depth - 12-18 inches\nDebris - 2/3 path\nCrown line - 6600'\nTerminus of debris - 4700'\nAspect - SE\ned2016-03-17 11:50:5610.173.132.2Ted2020-05-12 20:41:1764.91.61.64
89INFINITY2012-02-2811:09:00dunda_01SS5.0NR2D1ITR00NaN000000TKNaNNaNNaNFalse0.00.0False0.00.0Fracture line - 6400'ed2016-03-17 11:50:5610.173.132.2NaN0000-00-00 00:00:00NaN
910SHED092012-02-2811:16:00stein_01HS5.0NR3D2OTC342045NaN17123334241003065TK-UDFalse0.00.0False0.00.0<b>Upper Shed 9</b>\n\nFracture line - 6750'\nDebris toe - 5650'\nAspect - South through East\nCrown depth - 12"- 36"\nCrown width - 1400' \nStarting zone horizontal - left and center \nLikely wildlife triggered2016-03-17 11:50:5610.173.132.2Ted2020-05-12 21:28:0264.91.61.64

Last rows

avridavpathidavobsdateavobstimeavobserveridavtypeavtriggertypavtriggeravrelativesizeavdestructivesizeavinterfacesz_vertsz_horizsz_avgslopesz_elev_msz_aspectterm_elev_mav_vert_mcrown_width_mch_max_cmch_min_cmch_diff_cmterm_longpathterm_shortPathterm_detailterm_moistdeb_raildeb_rail_wdeb_rail_ddeb_hwydeb_hwy_wdeb_hwy_davcommentedcr_useredcr_timeedcr_ipeded_usereded_timeeded_ip
481499SHED09L2022-03-0112:20:00clark_01WL5.0NUR2D2ITC401710SW14003100000MR-UWFalse0.00.0False0.00.0NaNAdam2022-03-01 16:41:19170.49.113.17NaN0000-00-00 00:00:00NaN
482500FRYPAN2022-03-0114:41:00clark_01WL5.0NUR2D2ITC01850NE14603900000MR-UUFalse0.00.0False0.00.0SZ elevation estimatedAdam2022-03-01 16:43:55170.49.113.17NaN0000-00-00 00:00:00NaN
483501AP11832022-03-0105:00:00clark_01SS5.0NUR3D2.5ITR381737NE10676702151003568BR-1FWFalse0.00.0False0.00.01st observed on Mar. 2nd. then investigated the starting zone on Mar.7th. First initiated as a storm slab that failed at the new/old snow interface. Debris entrained a lot of wet snow on the way down and it was wet debris at the terminus which reached about 100 yards from the rail.Adam2022-03-08 17:43:1610.30.72.4NaN0000-00-00 00:00:00NaN
484502INFINITY2022-03-0211:35:00clark_01WL5.0NR2D1.5ITC401800S14004000000TR-UUFalse0.00.0False0.00.0One of the last avalanches observed during the Feb 28-Mar 2 cycle.Adam2022-03-15 13:38:5310.30.72.4NaN0000-00-00 00:00:00NaN
485503PATH11632022-03-1719:02:00clark_01WL5.0NUR1D1.5SML401830SE15502800000TK-UWFalse0.00.0False0.00.0NaNAdam2022-03-17 21:04:3110.30.72.4NaN0000-00-00 00:00:00NaN
486504SHED07W2022-03-0105:04:00clark_01SS5.0NCR2D2ITL382015E17602550000TK-UUFalse0.00.0False0.00.0Slab not observed until March 17th during a ski tour and could barely make out remnants of the crown. But this cornice fall was noted on March 1st,Adam2022-03-17 21:17:0410.30.72.4NaN0000-00-00 00:00:00NaN
487505AP1163H2022-03-2313:30:00clark_01WL5.0NUR1D1.5OTU01830SW14603700000TR-UWFalse0.00.0False0.00.0NaNAdam2022-03-23 21:32:4210.30.72.4NaN0000-00-00 00:00:00NaN
488506SHED102022-03-2313:30:00clark_01WL5.0NR2D2OTC402075SE16464290000TK-UWFalse0.00.0False0.00.0Obs on Mar. 24th from HwyAdam2022-03-28 16:30:3510.30.72.4NaN0000-00-00 00:00:00NaN
489507MCAMSER2022-03-2318:00:00clark_01HS5.0NCR3D3OTC402195SE17904052451504095MR-UUFalse0.00.0False0.00.0Obs on Mar. 24th from Hwy. Could not see runout from Hwy so runout details are estimated.Adam2022-03-28 16:34:4310.30.72.4NaN0000-00-00 00:00:00NaN
490508PATH11632022-03-2714:35:00clark_01WL5.0NR1D2OTL402135SE14007350000MR-UWFalse0.00.0False0.00.0Obs. on Mar. 28th from Hwy.Adam2022-03-28 16:37:0310.30.72.4NaN0000-00-00 00:00:00NaN